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Bias and inequity in palliative care disproportionately affect marginalised groups. Large language models (LLMs), such as GPT-4o, hold potential to enhance care but risk perpetuating biases present in their training data. This study aimed to systematically evaluate whether GPT-4o propagates biases in palliative care responses using adversarially designed datasets. In July 2024, GPT-4o was probed using the Palliative Care Adversarial Dataset (PCAD), and responses were evaluated by three palliative care experts in Canada and the United Kingdom using validated bias rubrics. The PCAD comprised PCAD-Direct (100 adversarial questions) and PCAD-Counterfactual (84 paired scenarios). These datasets targeted four care dimensions (access to care, pain management, advance care planning, and place of death preferences) and three identity axes (ethnicity, age, and diagnosis). Bias was detected in a substantial proportion of responses. For adversarial questions, the pooled bias rate was 0.33 (95% confidence interval [CI]: 0.28, 0.38); "allows biased premise" was the most frequently identified source of bias (0.47; 95% CI: 0.39, 0.55), such as failing to challenge stereotypes. For counterfactual s

This paper addresses the methodology for the quarterly estimation of Compensation of Employees paid by the General Government (GG) sector, in accordance with the European System of Accounts (ESA 2010). Due to the limited high-frequency data availability and the need to guarantee the consistency with annual constraints, quarterly estimation relies on indirect temporal disaggregation techniques. These methods use specific infra-annual indicators as proxies for the variables being estimated. The specific case of the quarterly estimation of Compensation of employees presents several additional challenges. Firstly, the information provided by the sources, based on cash or legal-accrual data, is elaborated to define indicators which respect the accrual ESA 2010 principle as the annual estimates, based on more compliant data sources such as final budgets of public entities. Secondly, at a quarterly level the extraordinary events - such as the recording of delayed collective bargaining agreements which result in arrears - have a strong impact on quarterly indicators, whereas their effect is mitigated at annual level. To attribute these flows to the period when the work is performed, multi-

Long-term care service for old people is in great demand in most of the aging societies. The number of nursing homes residents is increasing while the number of care providers is limited. Due to the care worker shortage, care to vulnerable older residents cannot be fully tailored to the unique needs and preference of each individual. This may bring negative impacts on health outcomes and quality of life among institutionalized older people. To improve care quality through personalized care planning and delivery with limited care workforce, we propose a new care planning model assisted by artificial intelligence. We apply bandit algorithms which optimize the clinical decision for care planning by adapting to the sequential feedback from the past decisions. We evaluate the proposed model on empirical data acquired from the Systems for Person-centered Elder Care (SPEC) study, a ICT-enhanced care management program.

CARE-link is an open-source, web-based clinical support platform designed to improve the management of gestational diabetes by linking clinicians and patients through an LLM-mediated workflow. The system aggregates patient-generated data outside the hospital, summarizes relevant clinical information, and delivers context-aware decision support to clinicians. For patients, CARE-link provides clear explanations of management plans and delivers timely lifestyle guidance through a WhatsApp interface. The integrated dual-facing design aims to promote continuous monitoring, support individualized care, and reduce the burden of in-clinic follow-ups. Built with a modular architecture, the platform can be adapted to other chronic conditions requiring longitudinal tracking and behavioral support. CARE-link has the potential to enhance clinical oversight, promote patient compliance, and strengthen continuity of care particularly in resource-constrained settings.

Relationship-centred care (RCC) recognises that healthcare quality depends not only on outcomes, but on how voice, responsibility, and emotional labour are negotiated among patients, caregivers, and providers. As AI systems enter sensitive care contexts, they introduce a new participant into these negotiations. Drawing on empirical work in Advance Care Planning (ACP) and peer support, we argue that AI's primary impact in high-subjectivity domains is not optimisation but redistribution: it reorganises who speaks, who decides, and who bears moral responsibility. Across both settings, participants were less concerned with technical accuracy than with relational consequences: whether AI would appropriately represent their decision, reduce burden, or blur accountability, scaffold connection, or subtly displace it. We identify three relational dimensions: authority, temporality, and visibility, through which AI reshapes care relationships, and propose design provocations centred on relational legibility, bounded agency, responsibility traceability, and non-substitutive scaffolding.

Digital Twin (DT) technology has emerged as a transformative approach in healthcare, but its application in personalized patient care remains limited. This paper aims to present a practical implementation of DT in the management of chronic diseases. We introduce a general DT framework for personalized care planning (DT4PCP), with the core components being a real-time virtual representation of a patient's health and emerging predictive models to enable adaptive, personalized care. We implemented the DT4PCP framework for managing Type 2 Diabetes (DT4PCP-T2D), enabling real-time collection of behavioral data from patients with T2D, predicting emergency department (ED) risks, simulating the effects of different interventions, and personalizing care strategies to reduce ED visits. The DT4PCP-T2D also integrates social determinants of health (SDoH) and other contextual data, offering a comprehensive view of the patient's health to ensure that care recommendations are tailored to individual needs. Through retrospective simulations, we demonstrate that integrating DTs in T2D management can lead to significant advancements in personalized medicine. This study underscores the potential of DT

Software engineering researchers repeatedly argue that the impact of their research on industrial practice, while desired and intended, is rarely achieved. We believe that a possible explanation of this phenomenon is the opposition of "caring about" and "caring for", based on the ethics of care. Indeed, while software engineering is collaborative and hence builds on interpersonal relations, researchers tend to care about "industrial impact" and "practitioners" in abstract terms, but rarely care for specific individuals working in specific contexts facing specific challenges. In this position paper, we advocate for the adoption of ethics of care in software engineering and discuss the implications of this adoption for researchers and conference organizers.

Despite a growing need for spiritual care in the US, it is often under-served, inaccessible, or misunderstood, while almost no prior work in CSCW/HCI research has engaged with professional chaplains and spiritual care providers. This interdisciplinary study aims to develop a foundational understanding of how spiritual care may (or may not) be expanded into online spaces -- especially focusing on anonymous, asynchronous, and text-based online communities. We conducted an exploratory mixed-methods study with chaplains (N=22) involving interviews and user testing sessions centered around Reddit support communities to understand participants' perspectives on technology and their ideations about the role of chaplaincy in prospective Online Spiritual Care Communities (OSCCs). Our Grounded Theory Method analysis highlighted benefits of OSCCs including: meeting patients where they are at; accessibility and scalability; and facilitating patient-initiated care. Chaplains highlighted how their presence in OSCCs could help with shaping peer interactions, moderation, synchronous chats for group care, and redirecting to external resources, while also raising important feasibility concerns, risks

The global ageing population necessitates new and emerging strategies for caring for older adults. In this article, we explore the potential for transformation in elderly care through Agentic Artificial Intelligence (AI), powered by Large Language Models (LLMs). We discuss the proactive and autonomous decision-making facilitated by Agentic AI in elderly care. Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older adults, represents important areas of application. With a potential for significant transformation of elderly care, Agentic AI also raises profound concerns about data privacy and security, decision independence, and access. We share key insights to emphasize the need for ethical safeguards, privacy protections, and transparent decision-making. Our goal in this article is to provide a balanced discussion of both the potential and the challenges associated with Agentic AI, and to provide insights into its responsible use in elderly care, to bring Agentic AI into harmony with the requirements and vulnerabilities specific to the elderly. Finally, we identify the priorities for the acad

We find ourselves on the ever-shifting cusp of an AI revolution -- with potentially metamorphic implications for the future practice of healthcare. For many, such innovations cannot come quickly enough; as healthcare systems worldwide struggle to keep up with the ever-changing needs of our populations. And yet, the potential of AI tools and systems to shape healthcare is as often approached with great trepidation as celebrated by health professionals and patients alike. These fears alight not only in the form of privacy and security concerns but for the potential of AI tools to reduce patients to datapoints and professionals to aggregators -- to make healthcare, in short, less caring. This infixated concern, we - as designers, developers and researchers of AI systems - believe it essential we tackle head on; if we are not only to overcome the AI implementation gap, but realise the potential of AI systems to truly augment human-centred practices of care. This, we argue we might yet achieve by realising newly-accessible practices of AI healthcare innovation, engaging providers, recipients and affected communities of care in the inclusive design of AI tools we may yet enthusiastically

Care deferral is the phenomenon where patients defer or are unable to receive healthcare services, such as seeing doctors, medications or planned surgery. Care deferral can be the result of patient decisions, service availability, service limitations, or restrictions due to cost. Continual care deferral in populations may lead to a decline in population health and compound health issues leading to higher social and financial costs in the long term. Consequently, identification of patients who may be at risk of deferring care is important towards improving population health and reducing care total costs. Additionally, minority and vulnerable populations are at a greater risk of care deferral due to socioeconomic factors. In this paper, we (a) address the problem of predicting care deferral for well-care visits; (b) observe that social determinants of health are relevant explanatory factors towards predicting care deferral, and (c) compute how fair the models are with respect to demographics, socioeconomic factors and selected comorbidities. Many health systems currently use rules-based techniques to retroactively identify patients who previously deferred care. The objective of this

This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care", a comprehensive framework designed to leverage Internet of Things (IoT) technology to enhance patient care, improve operational efficiency, and ensure data security in hospital settings. With the growing emphasis on utilizing advanced technologies in healthcare, this protocol aims to harness the potential of IoT devices to optimize patient monitoring, enable remote care, and support clinical decision-making. By integrating IoT seamlessly into nursing workflows and patient care plans, hospitals can achieve higher levels of patient-centric care and real-time data insights, leading to better treatment outcomes and resource allocation. This paper outlines the protocol's objectives, key components, and expected benefits, while emphasizing the importance of ethical considerations and ongoing evaluation to ensure successful implementation.

Granting LLMs direct control over costly, irreversible scientific experiments leads to unsafe exploration and unstable performance, but discarding LLM creativity entirely sacrifices significant optimization potential. We introduce CARE (Controlling LLM-Generated Policies through Auditable Review of Evidence in Scientific Experimentation), an auditable controller for high-throughput experimentation (HTE) optimization that keeps a non-LLM incumbent optimizer as the default action path while using LLMs to revise challenger ranking policies. Before each outcome is revealed, a public-evidence intervention gate compares the challenger with the incumbent. It authorizes the challenger's selection only when the evidence available before selection supports the change, with the decision recorded in the audit log. CARE outperforms all other evaluated methods on Minerva/Olympus and ChemLex benchmarks, with final-best improving from 80.0 to 88.5 on Minerva/Olympus and from 83.9 to 92.1 on ChemLex, relative to the public incumbent. Our experiments indicate that LLM self-evolution is more reliable when it expands the proposal space under an auditable controller, rather than directly choosing exper

Electronic health records (EHRs) provide comprehensive patient data which could be better used to enhance informed decision-making, resource allocation, and coordinated care, thereby optimising healthcare delivery. However, in mental healthcare, critical information, such as on risk factors, precipitants, and treatment responses, is often embedded in unstructured text, limiting the ability to automate at scale measures to identify and prioritise local populations and patients, which potentially hinders timely prevention and intervention. We describe the development and proof-of-concept implementation of VIEWER, a clinical informatics platform designed to enhance direct patient care and population health management by improving the accessibility and usability of EHR data. We further outline strategies that were employed in this work to foster informatics innovation through interdisciplinary and cross-organisational collaboration to support integrated, personalised care, and detail how these advancements were piloted and implemented within a large UK mental health National Health Service Foundation Trust to improve patient outcomes at an individual patient, clinician, clinical team,

As machine learning (ML) continues its rapid expansion, the environmental cost of model training and inference has become a critical societal concern. Existing benchmarks overwhelmingly focus on standard performance metrics such as accuracy, BLEU, or mAP, while largely ignoring energy consumption and carbon emissions. This single-objective evaluation paradigm is increasingly misaligned with the practical requirements of large-scale deployment, particularly in energy-constrained environments such as mobile devices, developing regions, and climate-aware enterprises. In this paper, we propose AI-CARE, an evaluation tool for reporting energy consumption, and carbon emissions of ML models. In addition, we introduce the carbon-performance tradeoff curve, an interpretable tool that visualizes the Pareto frontier between performance and carbon cost. We demonstrate, through theoretical analysis and empirical validation on representative ML workloads, that carbon-aware benchmarking changes the relative ranking of models and encourages architectures that are simultaneously accurate and environmentally responsible. Our proposal aims to shift the research community toward transparent, multi-obj

Unified diffusion editors often rely on a fixed, shared backbone for diverse tasks, suffering from task interference and poor adaptation to heterogeneous demands (e.g., local vs global, semantic vs photometric). In particular, prevalent ControlNet and OmniControl variants combine multiple conditioning signals (e.g., text, mask, reference) via static concatenation or additive adapters which cannot dynamically prioritize or suppress conflicting modalities, thus resulting in artifacts like color bleeding across mask boundaries, identity or style drift, and unpredictable behavior under multi-condition inputs. To address this, we propose Condition-Aware Routing of Experts (CARE-Edit) that aligns model computation with specific editing competencies. At its core, a lightweight latent-attention router assigns encoded diffusion tokens to four specialized experts--Text, Mask, Reference, and Base--based on multi-modal conditions and diffusion timesteps: (i) a Mask Repaint module first refines coarse user-defined masks for precise spatial guidance; (ii) the router applies sparse top-K selection to dynamically allocate computation to the most relevant experts; (iii) a Latent Mixture module subs

Relationship-centered care relies on trust and meaningful connection. As AI enters clinical settings, we must ask not just what it can do, but how it should be positioned to support these values. We examine a "middle, not top" approach where AI mediates communication without usurping human judgment. Through studies of CLEAR, an asynchronous messaging system, we show how this configuration addresses real-world constraints like time pressure and uneven health literacy. We find that mediator affordances (e.g., availability, neutrality) redistribute interpretive work and reduce relational friction. Ultimately, we frame AI mediation as relational infrastructure, highlighting critical design tensions around framing power and privacy.

Time constraints on doctor patient interaction and restricted access to specialists under the managed care system led to increasingly referring to computers as a medical information source and a self-health-care management tool. However, research show that less than 40% of information seekers indicated that online information helped them to make a decision about their health. Searching multiple web sites that need basic computer skills, lack of interaction and no face to face interaction in most search engines and some social issues, led us to develop a specialized life-like agent that would overcome mentioned problems.

Demand for health care is constantly increasing due to the ongoing demographic change, while at the same time health service providers face difficulties in finding skilled personnel. This creates pressure on health care systems around the world, such that the efficient, nationwide provision of primary health care has become one of society's greatest challenges. Due to the complexity of health care systems, unforeseen future events, and a frequent lack of data, analyzing and optimizing the performance of health care systems means tackling a wicked problem. To support this task for primary care, this paper introduces the hybrid agent-based simulation model SiM-Care. SiM-Care models the interactions of patients and primary care physicians on an individual level. By tracking agent interactions, it enables modelers to assess multiple key indicators such as patient waiting times and physician utilization. Based on these indicators, primary care systems can be assessed and compared. Moreover, changes in the infrastructure, patient behavior, and service design can be directly evaluated. To showcase the opportunities offered by SiM-Care and aid model validation, we present a case study for